A major challenge for data center operators is how to deal with the increasing amount of traffic shuffled among servers (e.g., “east-west” traffic generated by bandwidth-hungry applications). Some operators deal with this challenge by deploying advanced network topologies with more switches and links to expand network capacity. However, this requires extra infrastructure costs. In-network computation and in-network caching solutions are also proposed to reduce the traffic pressure to the network, but require modifications to the switch functionalities.
Various virtual machine (VM) placement schemes have been suggested to improve data center performance. Existing VM placement schemes aim at achieving performance goals such as consolidating multiple VMs into a physical server to improve resource multiplexing and migrating VMs to different physical machines for load balancing. However, these VM placement schemes do not aim at reducing the total network traffic.
Traffic-aware virtual machine (VM) placement is an effective way to solve the bandwidth explosion problem in data centers without significantly increasing capital expenditures. The basic idea is to model the traffic-aware VM placement problem as a balanced minimum k-cut problem (BMKP), in which the VMs of a job are placed into different physical servers and the optimization goal is to minimize the total bandwidth utilization in the network. However, the BMKP solution oversimplifies the problem and is based on assumptions that may not hold true for most practical data centers.